spatstat.core (version 2.1-2)

pcf.ppp: Pair Correlation Function of Point Pattern


Estimates the pair correlation function of a point pattern using kernel methods.


# S3 method for ppp
pcf(X, …, r = NULL, kernel="epanechnikov", bw=NULL,
                    correction=c("translate", "Ripley"),
                    divisor = c("r", "d"),
                    var.approx = FALSE,
                    ratio=FALSE, close=NULL)



A point pattern (object of class "ppp").


Vector of values for the argument \(r\) at which \(g(r)\) should be evaluated. There is a sensible default.


Choice of smoothing kernel, passed to density.default.


Bandwidth for smoothing kernel, passed to density.default. Either a single numeric value giving the standard deviation of the kernel, or a character string specifying a bandwidth selection rule recognised by density.default. If bw is missing or NULL, the default value is computed using Stoyan's rule of thumb: see Details.

Other arguments passed to the kernel density estimation function density.default.


Coefficient for Stoyan's bandwidth selection rule; see Details.


Edge correction. A character vector specifying the choice (or choices) of edge correction. See Details.


Choice of divisor in the estimation formula: either "r" (the default) or "d". See Details.


Logical value indicating whether to compute an analytic approximation to the variance of the estimated pair correlation.


Optional. Calculations will be restricted to this subset of the window. See Details.


Logical. If TRUE, the numerator and denominator of each edge-corrected estimate will also be saved, for use in analysing replicated point patterns.


Advanced use only. Precomputed data. See section on Advanced Use.


A function value table (object of class "fv"). Essentially a data frame containing the variables


the vector of values of the argument \(r\) at which the pair correlation function \(g(r)\) has been estimated


vector of values equal to 1, the theoretical value of \(g(r)\) for the Poisson process


vector of values of \(g(r)\) estimated by translation correction


vector of values of \(g(r)\) estimated by Ripley isotropic correction


vector of approximate values of the variance of the estimate of \(g(r)\)

as required.

If ratio=TRUE then the return value also has two attributes called "numerator" and "denominator" which are "fv" objects containing the numerators and denominators of each estimate of g(r).

The return value also has an attribute "bw" giving the smoothing bandwidth that was used.

Advanced Use

To perform the same computation using several different bandwidths bw, it is efficient to use the argument close. This should be the result of closepairs(X, rmax) for a suitably large value of rmax, namely rmax >= max(r) + 3 * bw.


The pair correlation function \(g(r)\) is a summary of the dependence between points in a spatial point process. The best intuitive interpretation is the following: the probability \(p(r)\) of finding two points at locations \(x\) and \(y\) separated by a distance \(r\) is equal to $$ p(r) = \lambda^2 g(r) \,{\rm d}x \, {\rm d}y $$ where \(\lambda\) is the intensity of the point process. For a completely random (uniform Poisson) process, \(p(r) = \lambda^2 \,{\rm d}x \, {\rm d}y\) so \(g(r) = 1\). Formally, the pair correlation function of a stationary point process is defined by $$ g(r) = \frac{K'(r)}{2\pi r} $$ where \(K'(r)\) is the derivative of \(K(r)\), the reduced second moment function (aka ``Ripley's \(K\) function'') of the point process. See Kest for information about \(K(r)\).

For a stationary Poisson process, the pair correlation function is identically equal to 1. Values \(g(r) < 1\) suggest inhibition between points; values greater than 1 suggest clustering.

This routine computes an estimate of \(g(r)\) by kernel smoothing.

  • If divisor="r" (the default), then the standard kernel estimator (Stoyan and Stoyan, 1994, pages 284--285) is used. By default, the recommendations of Stoyan and Stoyan (1994) are followed exactly.

  • If divisor="d" then a modified estimator is used: the contribution from an interpoint distance \(d_{ij}\) to the estimate of \(g(r)\) is divided by \(d_{ij}\) instead of dividing by \(r\). This usually improves the bias of the estimator when \(r\) is close to zero.

There is also a choice of spatial edge corrections (which are needed to avoid bias due to edge effects associated with the boundary of the spatial window):

  • If correction="translate" or correction="translation" then the translation correction is used. For divisor="r" the translation-corrected estimate is given in equation (15.15), page 284 of Stoyan and Stoyan (1994).

  • If correction="Ripley" or correction="isotropic" then Ripley's isotropic edge correction is used. For divisor="r" the isotropic-corrected estimate is given in equation (15.18), page 285 of Stoyan and Stoyan (1994).

  • If correction="none" then no edge correction is used, that is, an uncorrected estimate is computed.

Multiple corrections can be selected. The default is correction=c("translate", "Ripley").

Alternatively correction="all" selects all options; correction="best" selects the option which has the best statistical performance; correction="good" selects the option which is the best compromise between statistical performance and speed of computation.

The choice of smoothing kernel is controlled by the argument kernel which is passed to density.default. The default is the Epanechnikov kernel, recommended by Stoyan and Stoyan (1994, page 285).

The bandwidth of the smoothing kernel can be controlled by the argument bw. Bandwidth is defined as the standard deviation of the kernel; see the documentation for density.default. For the Epanechnikov kernel with half-width h, the argument bw is equivalent to \(h/\sqrt{5}\).

Stoyan and Stoyan (1994, page 285) recommend using the Epanechnikov kernel with support \([-h,h]\) chosen by the rule of thumn \(h = c/\sqrt{\lambda}\), where \(\lambda\) is the (estimated) intensity of the point process, and \(c\) is a constant in the range from 0.1 to 0.2. See equation (15.16). If bw is missing or NULL, then this rule of thumb will be applied. The argument stoyan determines the value of \(c\). The smoothing bandwidth that was used in the calculation is returned as an attribute of the final result.

The argument r is the vector of values for the distance \(r\) at which \(g(r)\) should be evaluated. There is a sensible default. If it is specified, r must be a vector of increasing numbers starting from r[1] = 0, and max(r) must not exceed half the diameter of the window.

If the argument domain is given, estimation will be restricted to this region. That is, the estimate of \(g(r)\) will be based on pairs of points in which the first point lies inside domain and the second point is unrestricted. The argument domain should be a window (object of class "owin") or something acceptable to as.owin. It must be a subset of the window of the point pattern X.

To compute a confidence band for the true value of the pair correlation function, use lohboot.

If var.approx = TRUE, the variance of the estimate of the pair correlation will also be calculated using an analytic approximation (Illian et al, 2008, page 234) which is valid for stationary point processes which are not too clustered. This calculation is not yet implemented when the argument domain is given.


Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008) Statistical Analysis and Modelling of Spatial Point Patterns. Wiley.

Stoyan, D. and Stoyan, H. (1994) Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.

See Also

Kest, pcf, density.default, bw.stoyan, bw.pcf, lohboot.


  X <- simdat
# }
  p <- pcf(X)
  plot(p, main="pair correlation function for X")
  # indicates inhibition at distances r < 0.3

  pd <- pcf(X, divisor="d")

  # compare estimates
  plot(p, cbind(iso, theo) ~ r, col=c("blue", "red"),
         ylim.covers=0, main="", lwd=c(2,1), lty=c(1,3), legend=FALSE)
  plot(pd, iso ~ r, col="green", lwd=2, add=TRUE)
  legend("center", col=c("blue", "green"), lty=1, lwd=2,

  # calculate approximate variance and show POINTWISE confidence bands
  pv <- pcf(X, var.approx=TRUE)
  plot(pv, cbind(iso, iso+2*sqrt(v), iso-2*sqrt(v)) ~ r)
# }